{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arcpy,pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = arcpy.da.FeatureClassToNumPyArray(r\"E:\\work\\地统计插值\\weather.shp\",\n",
    "                                       [\"mean\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "q25 = numpy.percentile(dt[\"mean\"], 25)\n",
    "q50 = numpy.percentile(dt[\"mean\"], 50)\n",
    "q75 = numpy.percentile(dt[\"mean\"], 75)\n",
    "iqr = q75 - q25\n",
    "u = q75 + 1.5 * iqr\n",
    "d = q25 - 1.5 * iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "if u > max(dt[\"mean\"]):\n",
    "    u =  max(dt[\"mean\"])\n",
    "if d < min(dt[\"mean\"]):\n",
    "    d = min(dt[\"mean\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,8))\n",
    "plt.boxplot(dt[\"mean\"])\n",
    "plt.text(1.1,u,\"上边缘\")\n",
    "plt.text(1.1,d,\"下边缘\")\n",
    "plt.text(1.1,q25,\"下四分位（25%）\")\n",
    "plt.text(1.1,q50,\"中四分位（50%）\")\n",
    "plt.text(1.1,q75,\"上四分位（75%）\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
